We at Procal are looking for a savvy Machine Learning & Data Engineer to join our team of analytics
experts to help us extract value from our data. You will lead all the processes from data collection,
cleaning, and preprocessing, to training models and deploying them to production. On a high level
we are looking for very hands-on engineers with good experience on big data, data architecture,
machine learning, and LLM.
The ideal candidate will be passionate about artificial intelligence and stay up to date with the
latest developments in the field.
This position will be a combination of typical Data Scientist math and analytical skills, with
research, advanced business, communication, and presentation skills.
Key Responsibilities
- Develop big data scalable solutions using Hadoop, Hive, Spark, Map-Reduce, Java, Python.
- Design schema and data molding for NoSQL Database & Data Warehouse.
- Develop ETL data flow and Cloud Integration to build reporting solutions.
- Assemble large, complex data sets that meet functional / non-functional requirements.
- Identify, design, and implement internal process improvements : automating manual
processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of datafrom a wide variety of data sources using SQL and Spark 'big data' technologies.
Designs, develops, codes, and troubleshoots with consideration of upstream anddownstream systems and technical implications.
Applies knowledge of tools within the Software Development Life Cycle toolchain to improvethe value realized by automation.
Applies technical troubleshooting to break down solutions and solve technical problems ofbasic complexity.
Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problemsand contribute to decision-making in service of secure, stable application development.
Verifying data quality, and / or ensuring it via data cleaning.Exploring and visualizing data to gain an understanding of it, then identifying differences indata distribution that could affect performance when deploying the model in the real world.
Understanding business objectives and developing models that help to achieve them, alongwith metrics to track their progress.
Managing available resources such as hardware, data, and personnel so that deadlines aremet.
Designing, developing, and researching Machine Learning systems, models, and schemesStudying, transforming, and converting data science prototypesPerforming statistical analysis and using results to improve models.Training and retraining Client systems and models as needed.Analyzing the use cases of Client algorithms and ranking them by their success probabilityUnderstanding when your findings can be applied to business decisions.Enriching existing Client frameworks and libraries.Build efficient pipeline to host LLM service in local machine.Develop high scalable RAG system combining with LLM to serve daily analysis andtroubleshooting.
Key Skill sets
Good Communication and presentation skillsTeam playerExperience in R and / or Python required.Proficiency with a deep learning framework such as TensorFlow or Keras.Proficiency with Python and basic libraries for machine learning such as scikit-learn andpandas.
Expertise in visualizing and manipulating big datasets.Good understanding of AI / Client stack - GPUs, MLFlow, LLM modelsHands-on practical experience in Java, Scala and / or Python, system design, applicationdevelopment, testing, and operational stability
Experience in developing, debugging, and maintaining code in a large corporate environmentwith one or more modern programming languages and database querying languages
Experience across the whole Software Development Life CycleExposure to agile methodologies such as CI / CD, Applicant Resiliency, and SecurityEmerging knowledge of software applications and technical processes within a technicaldiscipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.
Knowledge of Unix shell and SQL as well as NoSQL DBs is required.Experience with Linux, Spark, and Kafka.Good understanding of Large Language Model from system engineering perspective.Qualifications
MS or PhD in a relevant field (Computer Science, Engineering, Statistics, Physics, AppliedMath)
5 years of experience with Python to analyze datasets, train , evaluate, deploy, and optimizemodels.
3 Experience with Client frameworks such as PyTorch, TensorFlow, or similar3 years of machine learning / statistical modeling data analysis tools and techniques, andparameters that affect their performance experience.
1 year experience working with technologies related to large language models including LLMarchitectures, model evaluation, adapters, model customization including pre-training and
fine-tuning techniques.
Proficient with design, deployment, and evaluation of LLM-powered agents and tools andorchestration approaches.
Proficient with prompt engineering, embedding model fine tuning and retrieval methodevaluation and optimization approaches.
Master's degree in a quantitative field such as statistics, mathematics, data science,business analytics, economics, finance, engineering, or computer science
